R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(0.301029996
+ ,3
+ ,1.62324929
+ ,0.255272505
+ ,4
+ ,2.79518459
+ ,-0.15490196
+ ,4
+ ,2.255272505
+ ,0.591064607
+ ,1
+ ,1.544068044
+ ,0
+ ,4
+ ,2.593286067
+ ,0.556302501
+ ,1
+ ,1.799340549
+ ,0.146128036
+ ,1
+ ,2.361727836
+ ,0.176091259
+ ,4
+ ,2.049218023
+ ,-0.15490196
+ ,5
+ ,2.44870632
+ ,0.322219295
+ ,1
+ ,1.62324929
+ ,0
+ ,2
+ ,1.447158031
+ ,0.612783857
+ ,2
+ ,1.62324929
+ ,0.079181246
+ ,2
+ ,2.079181246
+ ,-0.301029996
+ ,5
+ ,2.170261715
+ ,0.531478917
+ ,2
+ ,1.204119983
+ ,0.176091259
+ ,1
+ ,2.491361694
+ ,0.531478917
+ ,3
+ ,1.447158031
+ ,-0.096910013
+ ,4
+ ,1.832508913
+ ,-0.096910013
+ ,5
+ ,2.526339277
+ ,0.146128036
+ ,4
+ ,1.33243846
+ ,0.301029996
+ ,1
+ ,1.698970004
+ ,0.278753601
+ ,1
+ ,2.426511261
+ ,0.113943352
+ ,3
+ ,1.278753601
+ ,0.301029996
+ ,3
+ ,1.477121255
+ ,0.748188027
+ ,1
+ ,1.079181246
+ ,0.491361694
+ ,1
+ ,2.079181246
+ ,0.255272505
+ ,2
+ ,2.146128036
+ ,-0.045757491
+ ,4
+ ,2.230448921
+ ,0.255272505
+ ,2
+ ,1.230448921
+ ,0.278753601
+ ,4
+ ,2.06069784
+ ,-0.045757491
+ ,5
+ ,1.491361694
+ ,0.414973348
+ ,3
+ ,1.322219295
+ ,0.380211242
+ ,1
+ ,1.716003344
+ ,0.079181246
+ ,2
+ ,2.214843848
+ ,-0.045757491
+ ,2
+ ,2.352182518
+ ,-0.301029996
+ ,3
+ ,2.352182518
+ ,-0.22184875
+ ,5
+ ,2.178976947
+ ,0.361727836
+ ,2
+ ,1.77815125
+ ,-0.301029996
+ ,3
+ ,2.301029996
+ ,0.414973348
+ ,2
+ ,1.662757832
+ ,-0.22184875
+ ,4
+ ,2.322219295
+ ,0.819543936
+ ,1
+ ,1.146128036)
+ ,dim=c(3
+ ,42)
+ ,dimnames=list(c('PS'
+ ,'D'
+ ,'Tg')
+ ,1:42))
> y <- array(NA,dim=c(3,42),dimnames=list(c('PS','D','Tg'),1:42))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
PS D Tg
1 0.30103000 3 1.623249
2 0.25527250 4 2.795185
3 -0.15490196 4 2.255273
4 0.59106461 1 1.544068
5 0.00000000 4 2.593286
6 0.55630250 1 1.799341
7 0.14612804 1 2.361728
8 0.17609126 4 2.049218
9 -0.15490196 5 2.448706
10 0.32221930 1 1.623249
11 0.00000000 2 1.447158
12 0.61278386 2 1.623249
13 0.07918125 2 2.079181
14 -0.30103000 5 2.170262
15 0.53147892 2 1.204120
16 0.17609126 1 2.491362
17 0.53147892 3 1.447158
18 -0.09691001 4 1.832509
19 -0.09691001 5 2.526339
20 0.14612804 4 1.332438
21 0.30103000 1 1.698970
22 0.27875360 1 2.426511
23 0.11394335 3 1.278754
24 0.30103000 3 1.477121
25 0.74818803 1 1.079181
26 0.49136169 1 2.079181
27 0.25527250 2 2.146128
28 -0.04575749 4 2.230449
29 0.25527250 2 1.230449
30 0.27875360 4 2.060698
31 -0.04575749 5 1.491362
32 0.41497335 3 1.322219
33 0.38021124 1 1.716003
34 0.07918125 2 2.214844
35 -0.04575749 2 2.352183
36 -0.30103000 3 2.352183
37 -0.22184875 5 2.178977
38 0.36172784 2 1.778151
39 -0.30103000 3 2.301030
40 0.41497335 2 1.662758
41 -0.22184875 4 2.322219
42 0.81954394 1 1.146128
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) D Tg
1.0129 -0.1110 -0.2765
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.39067 -0.13095 0.01591 0.13694 0.45938
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.01286 0.12507 8.098 7.00e-10 ***
D -0.11100 0.02207 -5.030 1.14e-05 ***
Tg -0.27653 0.06645 -4.162 0.000168 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1865 on 39 degrees of freedom
Multiple R-squared: 0.612, Adjusted R-squared: 0.5921
F-statistic: 30.75 on 2 and 39 DF, p-value: 9.61e-09
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.5605503 0.87889934 0.43944967
[2,] 0.7730192 0.45396167 0.22698084
[3,] 0.6781805 0.64363909 0.32181955
[4,] 0.5986194 0.80276116 0.40138058
[5,] 0.5516171 0.89676583 0.44838291
[6,] 0.8113760 0.37724796 0.18862398
[7,] 0.8974653 0.20506941 0.10253471
[8,] 0.8842604 0.23147930 0.11573965
[9,] 0.8804389 0.23912225 0.11956113
[10,] 0.8497501 0.30049984 0.15024992
[11,] 0.7981049 0.40379029 0.20189514
[12,] 0.8419065 0.31618703 0.15809351
[13,] 0.8283664 0.34326722 0.17163361
[14,] 0.8347145 0.33057101 0.16528551
[15,] 0.7720353 0.45592940 0.22796470
[16,] 0.7413705 0.51725894 0.25862947
[17,] 0.6706109 0.65877815 0.32938907
[18,] 0.7192533 0.56149339 0.28074670
[19,] 0.6350857 0.72982866 0.36491433
[20,] 0.5889750 0.82205002 0.41102501
[21,] 0.6000718 0.79985632 0.39992816
[22,] 0.5525413 0.89491743 0.44745872
[23,] 0.4839896 0.96797920 0.51601040
[24,] 0.6531282 0.69374350 0.34687175
[25,] 0.9693665 0.06126699 0.03063350
[26,] 0.9765522 0.04689559 0.02344780
[27,] 0.9694031 0.06119376 0.03059688
[28,] 0.9441648 0.11167035 0.05583518
[29,] 0.9256937 0.14861269 0.07430635
[30,] 0.9434211 0.11315773 0.05657887
[31,] 0.8875995 0.22480101 0.11240050
> postscript(file="/var/www/rcomp/tmp/1h14j1292346198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2h14j1292346198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3ss341292346198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4ss341292346198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5ss341292346198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 42
Frequency = 1
1 2 3 4 5 6
0.070054162 0.459376743 -0.100100866 0.116188440 0.148272770 0.152017433
7 8 9 10 11 12
-0.102638768 0.173911628 0.064391916 -0.130760698 -0.390672839 0.270805943
13 14 15 16 17 18
-0.136716744 -0.158735054 0.073598202 -0.036827593 0.251808158 -0.159016718
19 20 21 22 23 24
0.143851885 -0.054264314 -0.131010772 0.047901508 -0.212296676 0.029645036
25 26 27 28 29 30
0.144755633 0.164461625 0.057887466 0.002179080 -0.195327408 0.279748511
31 32 33 34 35 36
-0.091200350 0.100752989 -0.047119257 -0.099201649 -0.186161805 -0.330432230
37 38 39 40 41 42
-0.077143764 0.062585321 -0.344577556 0.083920823 -0.148534704 0.234624493
> postscript(file="/var/www/rcomp/tmp/62j3p1292346198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 42
Frequency = 1
lag(myerror, k = 1) myerror
0 0.070054162 NA
1 0.459376743 0.070054162
2 -0.100100866 0.459376743
3 0.116188440 -0.100100866
4 0.148272770 0.116188440
5 0.152017433 0.148272770
6 -0.102638768 0.152017433
7 0.173911628 -0.102638768
8 0.064391916 0.173911628
9 -0.130760698 0.064391916
10 -0.390672839 -0.130760698
11 0.270805943 -0.390672839
12 -0.136716744 0.270805943
13 -0.158735054 -0.136716744
14 0.073598202 -0.158735054
15 -0.036827593 0.073598202
16 0.251808158 -0.036827593
17 -0.159016718 0.251808158
18 0.143851885 -0.159016718
19 -0.054264314 0.143851885
20 -0.131010772 -0.054264314
21 0.047901508 -0.131010772
22 -0.212296676 0.047901508
23 0.029645036 -0.212296676
24 0.144755633 0.029645036
25 0.164461625 0.144755633
26 0.057887466 0.164461625
27 0.002179080 0.057887466
28 -0.195327408 0.002179080
29 0.279748511 -0.195327408
30 -0.091200350 0.279748511
31 0.100752989 -0.091200350
32 -0.047119257 0.100752989
33 -0.099201649 -0.047119257
34 -0.186161805 -0.099201649
35 -0.330432230 -0.186161805
36 -0.077143764 -0.330432230
37 0.062585321 -0.077143764
38 -0.344577556 0.062585321
39 0.083920823 -0.344577556
40 -0.148534704 0.083920823
41 0.234624493 -0.148534704
42 NA 0.234624493
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.459376743 0.070054162
[2,] -0.100100866 0.459376743
[3,] 0.116188440 -0.100100866
[4,] 0.148272770 0.116188440
[5,] 0.152017433 0.148272770
[6,] -0.102638768 0.152017433
[7,] 0.173911628 -0.102638768
[8,] 0.064391916 0.173911628
[9,] -0.130760698 0.064391916
[10,] -0.390672839 -0.130760698
[11,] 0.270805943 -0.390672839
[12,] -0.136716744 0.270805943
[13,] -0.158735054 -0.136716744
[14,] 0.073598202 -0.158735054
[15,] -0.036827593 0.073598202
[16,] 0.251808158 -0.036827593
[17,] -0.159016718 0.251808158
[18,] 0.143851885 -0.159016718
[19,] -0.054264314 0.143851885
[20,] -0.131010772 -0.054264314
[21,] 0.047901508 -0.131010772
[22,] -0.212296676 0.047901508
[23,] 0.029645036 -0.212296676
[24,] 0.144755633 0.029645036
[25,] 0.164461625 0.144755633
[26,] 0.057887466 0.164461625
[27,] 0.002179080 0.057887466
[28,] -0.195327408 0.002179080
[29,] 0.279748511 -0.195327408
[30,] -0.091200350 0.279748511
[31,] 0.100752989 -0.091200350
[32,] -0.047119257 0.100752989
[33,] -0.099201649 -0.047119257
[34,] -0.186161805 -0.099201649
[35,] -0.330432230 -0.186161805
[36,] -0.077143764 -0.330432230
[37,] 0.062585321 -0.077143764
[38,] -0.344577556 0.062585321
[39,] 0.083920823 -0.344577556
[40,] -0.148534704 0.083920823
[41,] 0.234624493 -0.148534704
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.459376743 0.070054162
2 -0.100100866 0.459376743
3 0.116188440 -0.100100866
4 0.148272770 0.116188440
5 0.152017433 0.148272770
6 -0.102638768 0.152017433
7 0.173911628 -0.102638768
8 0.064391916 0.173911628
9 -0.130760698 0.064391916
10 -0.390672839 -0.130760698
11 0.270805943 -0.390672839
12 -0.136716744 0.270805943
13 -0.158735054 -0.136716744
14 0.073598202 -0.158735054
15 -0.036827593 0.073598202
16 0.251808158 -0.036827593
17 -0.159016718 0.251808158
18 0.143851885 -0.159016718
19 -0.054264314 0.143851885
20 -0.131010772 -0.054264314
21 0.047901508 -0.131010772
22 -0.212296676 0.047901508
23 0.029645036 -0.212296676
24 0.144755633 0.029645036
25 0.164461625 0.144755633
26 0.057887466 0.164461625
27 0.002179080 0.057887466
28 -0.195327408 0.002179080
29 0.279748511 -0.195327408
30 -0.091200350 0.279748511
31 0.100752989 -0.091200350
32 -0.047119257 0.100752989
33 -0.099201649 -0.047119257
34 -0.186161805 -0.099201649
35 -0.330432230 -0.186161805
36 -0.077143764 -0.330432230
37 0.062585321 -0.077143764
38 -0.344577556 0.062585321
39 0.083920823 -0.344577556
40 -0.148534704 0.083920823
41 0.234624493 -0.148534704
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7daka1292346198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8daka1292346198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9daka1292346198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/106kjv1292346198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/1192hj1292346198.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12v3y71292346198.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/139uwx1292346198.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14cdul1292346198.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15gebr1292346198.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/161w9f1292346198.tab")
+ }
>
> try(system("convert tmp/1h14j1292346198.ps tmp/1h14j1292346198.png",intern=TRUE))
character(0)
> try(system("convert tmp/2h14j1292346198.ps tmp/2h14j1292346198.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ss341292346198.ps tmp/3ss341292346198.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ss341292346198.ps tmp/4ss341292346198.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ss341292346198.ps tmp/5ss341292346198.png",intern=TRUE))
character(0)
> try(system("convert tmp/62j3p1292346198.ps tmp/62j3p1292346198.png",intern=TRUE))
character(0)
> try(system("convert tmp/7daka1292346198.ps tmp/7daka1292346198.png",intern=TRUE))
character(0)
> try(system("convert tmp/8daka1292346198.ps tmp/8daka1292346198.png",intern=TRUE))
character(0)
> try(system("convert tmp/9daka1292346198.ps tmp/9daka1292346198.png",intern=TRUE))
character(0)
> try(system("convert tmp/106kjv1292346198.ps tmp/106kjv1292346198.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.020 1.660 4.654